Anonymity Handling and Sensor Object Modeling for Pervasive Environments using VISA Processing

International Journal of Information Technology & Computer Science ( www.ijitcs.com ) Volume 15 Issue No 1, June 2014

ISSN ( online ) : 2091-1610

This paper was presented @International Conference on Future Computing & Communication System ( ICFCCS 2014 ) ( www.icfccs.com ) –

Dubai, UAE on May 11-12, 2014 : Copyright © International Journal of Information Technology & Computer Science ( IJITCS ) .

37

Anonymity Handling and Sensor Object Modeling for Pervasive Environments using

VISA Processing

Dr.G.Radhamani

School of IT and Science, Dr.GRD College of Science, Coimbatore, India

[email protected]

K.Vanitha

School of IT and Science, Dr.GRD College of Science, Coimbatore, India

[email protected]

Angela Amphawan

Research Laboratory of Electronics

Electrical Engineering and Computer Science Department

Massachusetts Institute of Technology, 02139 Cambridge, USA

[email protected]

Abstract:

Pervasive computing devices are configured for anywhere at any time services to adapt the changes in the dynamic

environment with respect to the user mobility. The paper proposes a salient model for a log based anonymity handling

in pervasive networks. The model defines a resource registry and sensor object modeling. Associability between the

nodes are configured with an allied user interface in the environment. Resource registry refers to a record of

information about the sensor nodes. A log book is maintained to keep track of the service discovery issues. New device

enquiry, registration and adaption are major issues in such pervasive devices. These issues lead to unacknowledged

communication that causes anonymity. This paper defines and discusses the presence of anonymity in pervasive

environments due to aforementioned issues.

Keywords- : Pervasive Computing; Service Discovery; Anonymity; Middleware.

I. INTRODUCTION

Personal computing is expanded to pervasive computing when the user interaction increases to many devices

or computers. Pervasive computing provides an attractive vision for accessing information anywhere and anytime.

Such environments gracefully integrate networked computing devices from tiny sensors to extremely dynamic and

powerful devices with people and their ambient environments. A room, for example, might be saturated with hundreds

of devices that provide information to people without seeking their active attention. Pervasive computing is the idea

that almost any type of device can be embedded with chips to connect the device to an infinite network of other devices

to provide convenient access to relevant information when and where it is needed. With respect to the development

of applications for pervasive environments, a middleware can be used to bridge the gap between the application and

the underlying operating systems and networks. One of the basic purposes of any middleware is to satisfy application

requirements as well as to support the challenges and issues in computing devices. Dynamic changes of the

environment reflect in the mobility and location change of the user or the device, data transfer between sensor nodes

Owner

Typewriter

Owner

Typewriter

Owner

Typewriter


ISSN ( online ) : 2091-1610



38

and any state change information. Since the devices are mostly invisible across the users computing environment,

there may be possibility of unknown devices or unknown user emerging in existing computing environment to access

resources. This possibility renders the anonymity in pervasive environments. Handling anonymity using object-based

registration and log-based reusability are the key contributions of the paper.

II. LITERATURE REVIEW

Several middleware are available for pervasive systems. Some of the most representative are described in this section.

[1] focused on some important middleware categories and technologies suitable for today’s mobile middleware and

discussed about how the middleware is connected to the pervasive environment. [2] presented the concept and design

of BASE, a flexible middleware supporting the additional requirements of pervasive computing environments, and

how the design implementation of BASE middleware shields applications from the multitude of different

communication technologies and interoperability protocols by separating the communication model of the application

and the interoperability protocols used.[3] discussed a privacy-aware solution for service discovery in heterogeneous

networks, based on the MUSDAC platform and privacy issues that arise during service discovery and mechanisms to

control disclosure of private information contained in service related data. [4] described about the privacy threats

identified in a pervasive environment and presented a set of principles for ensuring privacy.

Number of privacy protection mechanisms for pervasive systems were examined with the focus on the level

of anonymity offered to the end user. Also Stelios Dritsas et al concluded by presenting a set of essential actions one

should take into account, in order to ensure users anonymity in a pervasive computing environment. [5] Provides an

object-based framework for supporting context-sensitive applications. [6] Presented the Sensor Enablement for the

Average Programmer (SEAP) middleware, which lowers the barrier to entry for programming pervasive computing

applications with existing languages. A Virtualized Secure Framework between mobile devices (clients) and Location

Based Services (LBS) Server in Virtual Machine (VM) environment based on Direct Anonymous Attestation protocol

is proposed. Trusted Platform Module (TPM) acts as the foundation for mobile security mechanisms and privacy of

user's information.[13]. The privacy for internet of things technologies by focusing on the most "primitive" members,

bare sensors and RFIDs is addressed. [14]. Classification of anonymity in pervasive environments is discussed and a

handling model is suggested. [15].

III. ANONYMITY IN PERVASIVE COMPUTING

Anonymity can be defined as a subject who is not identifiable or unacknowledged among the set of subjects.

In pervasive computing, anonymity plays a key role in user, device and data components, as illustrated in Fig. 1. It

can be of any device or user which is unidentifiable by other devices among them. Information can also be anonymous

in pervasive environments.


ISSN ( online ) : 2091-1610



39

Fig.1. Anonymity in Pervasive Environment

A. Device or User Anonymity

Each device in pervasive computing environment performs their role for a user to satisfy their requirements

and makes adaptable to the changes in the physical world. An anonymous user is a user who unknowingly enters into

its computing world. An unacknowledged user may provide some malicious access to the other devices to destroy in

it. The anonymous user may steal the personal information such as user personal data (trusted entity in the pervasive

environment) device identity, location etc among other user in the pervasive systems. Ubiquitous and pervasive

devices are becoming very popular and less expensive these days with which different devices like mobiles, smart

phones and PDA etc has been dynamically adding into the computing environment to interact and coordinate easily

with other existing devices in the pervasive environment .due to this dynamism any device entering into the existing

pervasive environment may be a malicious one to abuse the service or resources available there or anonymously

interact with all the devices in the computing environment to access the resources. Since pervasive computing

permeates, dynamism in addition and removal of devices leads to misidentification or anonymity in Pervasive

computing.

B. Data Anonymity

The pervasive applications may need the user’s static or dynamic data to provide access to services. Static

data may include age, group, education level, etc. Dynamic data refers to contextual information such a location,

activity state, etc. The static profile and contextual information are exchanged often among devices in the dynamic

environment. The owner of the information desires control of what goes out of the system. On the other hand, the

service provider requires a certain level of quality of the information disclosed in order to provide the service. The

greater the amount of information disclosed, the higher the chance of re –identification of the user even if the identity

of the user is not disclosed. The balance hinges between the user’s desire to control the anonymity level of the

information disclosed and the provider’s requirement of meeting a quality level of that information.

IV. MODELING SENSOR SERVICES IN PERVASIVE COMPUTING

Service discovery is an increasingly important issue as we move towards realizing pervasive systems. How

does the user identify and access a particular service from the plethora of services that may potentially be available

around him at any moment? Having discovered a service, a second important issue is how the user interacts with


ISSN ( online ) : 2091-1610



40

it. Pervasive computing systems are not like the common desktop PC network, but are composed of smallembedded

devices, communicating in a wireless network independent of any global management. This is where the field of

service discovery fits in. For a device to be truly mobile, it must be able to interface and co-ordinate with its

surroundings without the user’s intervention. For this to happen, the service discovery protocol must be able to

discover local resources and form an ad-hoc network. Thus, ubiquitous service discovery is the ability to discover and

form an ad-hoc network without explicit user direction. A typical service discovery protocol has ten main components.

They are service and attribute naming, initial communication method, discovery and registration, service discovery

infrastructure, service information state, discovery scope, service selection, service invocation, service usage and

service status inquiry. Pervasive computing in sensor networks is different from regular networks. The reason is the

sources of data are tiny sensors that are attached to a Sink node.

A. Transforming Sensor Data as an Object

From a computing perspective, a user interface would interact with sensing signals so as to transform them to an

understandable format. Every sensed data is to be converted as an Object and deployed in the registry. Object creation

is a process of memory allocation, acquiring state information and recording. Thus an object will have life span and

expiry time. Fig.2 illustrates a Sensor Object to be stored in the registry.

Fig.2 A Sensor Object

B. Object Registration

Every deployed sensor is accounted in the Object Registry in the Pervasive Environments. Object Registry is a

directory maintained in Pervasive Server. The Server is operated as a dynamic or stationary node based on the

application domain. The proposed model describes with sensor networks in an agriculture farm. The farm is said to

Pervasive since the nodes are minimum visible, autonomous and cognitive. The end users of the Pervasive farm are

to be plants and moving cattle. Obviously, the network is mandatorily self-sustained. Initial registration of sensor

objects is probably started manually by human interaction. Over the period of time, the network receives registered

and unregistered entities within the boundary. The network is responsible to account the unknown signals.


ISSN ( online ) : 2091-1610



41

V. LOG BASED HANDLING

A. Design of the Log

The log records the transactions between the sensor nodes and the user every time. The log is designed with a Log

Run Number (LRN) that is generated automatically. Creation of the Log is made when the sub-net of the sensors

nodes are deployed. Anonymity handler is a software component in Pervasive Server Application that checks for the

registered entries. The Log is classified into (a) Transaction Log and (b) Guest Registry. The Transaction Log (TL) is

a record of routine signal passing between the nodes or to the base station vice versa. The Table 1 illustrates the

transaction log template that maintains the data transfer between nodes and user. A special field Attest (ATS) is created

for checking the authentication of the registered user.

Table 1. Transaction Log Template

ATS is valued by Pervasive Server attached to the Network. If the value recorded as ‘1’, the data transfer is valid.

Otherwise the value is set as ‘0’ for anonymous entry.

B. Guest Registry (GR)

The Transaction Log pours out the data to Guest Registry if the value of ATS IS ‘0’. i.e. the book considers

the anonymous entry as a guest objects. The Guest Registry (GR) is responsible to keep track of unregistered entries

passed from TL. All anonymous entries are not malicious ones. Since, Pervasive Networks avail the proactive

identification some nearby cluster sensors are possible. So many times the need for including a guest as an

authenticated Sensor Object happens. Table 2 refers the Guest Registry Log in a farm region scenario.

Table 2. GUEST REGISTRY IN FARMING SCENARIO

C. The Data log

The nodes are sending signals to the sink nodes synchronously, wherein the time interval is fixed. And the signals are

the similar to the prior in most of the cases. The replicated signals are to be filtered in the sink end, called data filtering.

The data log is maintained at the sink end for accounting the data for further classification and decision making. The

refined data log is generated with registered sensors. If any unknown signals are identified in the network, the Sink


ISSN ( online ) : 2091-1610



42

node and base station would enquire the Pervasive Server for the verification. The Pervasive Server replies to the

base station with guest acceptance or for rejection of new service.

VI. THE PROPOSED MODEL

Middleware is a software layer which sits below the applications and above the operating system to provide

the common programming abstraction across all the distributed systems. Context-awareness, dynamism and

heterogeneity are some of the properties that differentiate pervasive computing from traditional distributed systems.

Most traditional distributed systems are unaware of context, are static, and are composed of homogeneous devices.

Furthermore, different devices might be connected to different networks, with different latency and bandwidth. As a

result, the middleware must provide mechanisms for handling disconnections, addressing fault tolerance, and adapting

to a number of issues related to diversity including heterogeneous device resources. Many of the existing

middleware’s addresses the pervasive computing environment issues such as mobility, disconnection, dynamic

introduction, removal of devices and merging of the physical environment with the computational infrastructure etc

to provide a convenient interaction among the application and the pervasive devices. Even though middleware’s

which provides service such as privacy they can only protect the personal details. Due to the anonymity issues in

pervasive systems may get into the risks such as personal information healing, memory destruction, routing failure

can be occurred. Fig. 3 is the proposed middleware architecture which tackles the anonymity issues in pervasive

systems.

The middleware architecture for handling anonymity deals with three components such as anonymity

handler, Resource registry and device registry. Device registry contains the details about trusted devices (managing

by the provider) and also it updates the device which is new to its computing environment. Resource Registry holds

the details about the resources available for the devices in its local region. Anonymity handler is used to validate the

equipments in pervasive systems to identify whether it can be acknowledgeable one or unknown one. It allows the

registry to update the record if it is a valid device otherwise it will eliminate. Since services are on the opposite end

from the client, it is difficult for a service to force itself upon the client.

Fig.3 Anonymity Handler inMiddleware


ISSN ( online ) : 2091-1610



43

None of the reviewed solutions employ this type of selection. This type is included to cover all possible decision

making points in the service discovery process. Decisions by the Client Application A majority of the solutions select

services on the client-end. After receiving a list of feasible services, the client application chooses a service based on

service information and additional information provided by the user, operating system, and other bodies. Using this

type of decision making process, though, the client must receive a full or partial list of available services. The Fig.4

illustrates the connectivity between the components such as Pervasive Server, Anonymity handler with a Base Station

and a cluster of Sensor Nodes. A set of sensor nodes deployed in an environment attached to a base station. During

the sensing time, the signals are verified for the registered owners by reaching the base station. If any exceptions carry

for the unknown entry, the process of anonymity finding begins. The Pervasive server will be asked for the unknown

entry to be added or rejected.

Fig.4 The Class diagram with relationships

Based on the decision making process with guest log based interpretation, the Pervasive Server reports to the base

station either for allow or for reject.


ISSN ( online ) : 2091-1610



44

Fig.5 The cardinality between users and nodes

The user can communicate to one or more devices during the real time sensing. In negation, a device can communicate

to multiple users. This paradigm is a mandatory process in Pervasive Environments. Many devices could interact with

an individual user or many users can interact with a device vice versa. The Cardinality relationship between the

devices and users are illustrated in Fig.5 above. The Service entry and dissemination could be carried in Many-to-

One aspect for the sensors.

A. VISA Processing Techniques for a Pervasive Space

VISA is a document provided to one for the entry/exit to a region for being some amount of time. Visa

provider must be the authority of the Region of Interest (ROI). The Person who receives Visa can be a visitor/guest to

the region. Based on the Guest Registry log management in this context of anonymity handling, a Pervasive Server is

responsible to issue the visa for a new arrival device or a user. Visa is classified multitude categories in many countries.

A Pervasive Space is assumed as a country to be visited and the device/user requires to get a visa from the Server.

Short-stay or visitor visa is the commonly required one. The Server identifies a new arrival once the boundary region

is touched. On-arrival visa is issued with a time stamp.

Entry Visa Processing Algorithm

Algorithm 1: Entry-Visa Processor

(entry permit and verification in a Pervasive Space)


ISSN ( online ) : 2091-1610



45


ISSN ( online ) : 2091-1610



46

In the above Fig.6., the routine data frequency is measured in y-axis, whereas the number of sensors nodes are in x-

axis. The signals far outnumbers from the replicate signals. The filtering of the replicate data is accounted. The node

‘n4’ has no replicated signals out which it transmitted only once. Whereas node ‘n6’ has three times replicated out of

six times transmitted. The frequency of replication is a random measure based on the environmental changes.

VII. CONCLUSION AND FUTURE SCOPE

The proposed model defines anonymity for pervasive computing at the middleware level and classifies the presence

of anonymity in unknown scenarios so as to open up the avenues of handling a new user as a guest not a un-trusted

entry. The log-based model for anonymous entries is also accounted. Visa processing algorithms for entry and exit

provides a virtual fencing to pervasive space. The key research issue is to handle unknown and unauthorized entities

into the pervasive networks. Even for the first time enquiry or missing registrations are to be focused for future

avenues. The guest registry and log for replicate signals are archived. Future work will be focused on the

implementation of the anonymity handler and to evaluate the performance at the application level.

REFERENCES

[1] Vesa Kautto (2001), “Middleware for Pervasive Computing ” HUT, Telecommunications Software and

Multimedia Laboratory.

[2] Christian Becker, Gregor Schiele, Holger Gubbels, and Kurt Rothermel “BASE -A Micro broker- Based

Middleware for Pervasive Computing ” University of Stuttgart Institute of Parallel and Distributed Systems

(IPVS), Breitwiesenstr. 20-22, 70565 Stuttgart, Germany

[3] Roberto Speicys Cardoso, Pierre-Guillaume Raverdy, and Valeŕie Issarny, “A Privacy-Aware Service Discovery

Middleware for Pervasive Environments” , Inria Rocquencourt 78153 Le Chesnay, France.

[4] Cardoso R.S., Raverdy, P.-G and Issarny, V., 2007, in IFIP International federation for Information Processing,

Volume 238, Trust Management, eds, Etalle, S., Marsh S.,(Boston:Springer) pp 59-74

[5] Stelios Dritsas, Dimitris Gritzalis a, Costas Lambrinoudakis “Protecting privacy and anonymity in pervasive

computing: trends and perspectives”, Volume 23 Issue 3, August 2006. pp 196-210

[6] Stephen S. Yau, Fariaz Karim, YuWang, Bin Wang, and Sandeep K.S. Gupta (July 2002), “ Reconfigurable

Context Sensitive Middleware for Pervasive Computing”, Arizona State Arizona State Volume 1 Issue 3, pp 33-

40 .

[7] Seth Holloway, Drew Stovall, Jorge Lara-Garduno, and Christine Julien, “Opening Pervasive Computing to the

Masses Using the SEAP Middleware ” Mobile and Pervasive Computing Group , The University of Texas at

Austin , March 2009.

[8] R. Jason Weiss, and J. Philip Craiger (2002), “Ubiquitous Computing ”, University of Nebraska–Omaha, Volume

39, Number 4, pp. 44 - 52.


ISSN ( online ) : 2091-1610



47

[9] Wassim Masri, Zoubir Mammeri (September 2007), "Middleware for Wireless Sensor Networks: A Comparative

Analysis," IFIP International Conference on Network and Parallel Computing Workshops. Pp 349-356

[10] Stephen S.Yau et al., (September 2002) Reconfigurable Context Sensitive Middleware for Pervasive Computing“,

IEEE Pervasive Computing,. pp 33-40

[11] Tinghuai Ma et al., (2011) “Privacy Preserving in Ubiquitous Computing: Classification and Hierarchy”,

COMSIS Journal, Vol 8, No. 4,. pp 1185-1206.

[12] Miyuki Imada, Masakatsu Ohta, Masayasu Yamaguchi, Sun Yong Kim, (2008) "LooM: An anonymity

quantification method in pervasive computing environments", International Journal of Pervasive Computing and

Communications, Vol. 4 Iss: 1, pp.110 – 123

[13] Chi Lin; Guowei Wu; Lin Yao; Zuosong Liu, "A Combined Clustering Scheme for Protecting Location Privacy

and Query Privacy in Pervasive Environments," Trust, Security and Privacy in Computing and Communications

(TrustCom), 2012 IEEE 11th International Conference on , vol., no., pp.943,948, 25-27 June 2012 doi:

10.1109/TrustCom.2012.19

[14] Trcek, D.; Brodnik, A., "Hard and soft security provisioning for computationally weak pervasive computing

systems in E-health," Wireless Communications, IEEE , vol.20, no.4, pp.22,29, August 2013.doi:

10.1109/MWC.2013.6590047

[15] G.Radhamani, K.Vanitha and D.Rajeswari. “Classification and Handling of Anonymity in Pervasive

Middleware”. International Journal of Computer Applications , Vol. 17, No.6, pp. 28-31, March 2011. Published

by Foundation of Computer Science.

Date post:	07-Feb-2023
Category:	Documents
Upload:	mit
View:	0 times
Download:	0 times

Anonymity Handling and Sensor Object Modeling for Pervasive Environments using VISA Processing

Documents